Incorporating predicted species distribution in adaptive and conventional sampling designs
Bibliographic record
Abstract
Introduction Monitoring rare and clustered populations is challenging because of the large effort required to encounter occupied habitat and yield precise population estimates (McDonald 2004). Sampling designs are available to help reduce the effort required to encounter occupied habitat and increase precision, including stratified sampling, probability proportional to size (PPS) sampling, and various adaptive sampling designs (Thompson 2002). Use of these designs is motivated, in an intuitive sense, by each design's ability to allocate more sampling effort where target species are (or are likely to be) and less where they are not. This intuitive approach to allocation of effort can lead to increased precision when variability in the population tends to be higher in areas of high species density or abundance (Box 17.1). Conventional designs, such as stratified and PPS sampling, rely on prior information to allocate effort. For example, prior information could come from predicted species or habitat distributions (Guisan and Zimmermann 2000, Le Lay et al . 2010). Use of prior information is not a basic property of adaptive sampling designs, but these designs could use such information when available. In this chapter, we demonstrate how prior information on species distribution can be incorporated into adaptive and conventional sampling designs. We start the chapter by introducing adaptive sampling, which remains a somewhat novel sampling design even though it was introduced by Thompson (1990) over 20 years ago. We then present a case study illustrating and evaluating the performance of conventional and adaptive sampling designs that either incorporate or ignore predicted species distributions. We examine how these designs compare in terms of efficiency, probability of sampling occupied habitat, and robustness to model inaccuracy. We end the chapter with recommendations and a discussion of future research and developments.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.024 | 0.052 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".